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Questions tagged [multimodality]

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Multi-View Survival Analysis

I have a data set containing various subsets of medical data about a cohort of patients. For example there are blood test results, demographics, medical examination results and a medical history among ...
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Is this Bayesian model averaging?

A classical example of Bayesian model averaging (BMA) is the regression setup where the choice of different sets of covariates corresponds to different models $\mathcal{M}_k$, $k = 1, \ldots, K$, ...
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1answer
55 views

Which statistical methods are best suited for distribution with two peaks?

My data shows this distribution: I am looking for a statistical distribution which my data follows. Thought about poisson distribution, but goodness of fit test shows p < 0.05
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How is 'domain adaptation' different from 'exploiting' multiple domains?

I am asking this question from context of transfer learning paradigm of machine learning. In transfer learning, we are given different domains one of which is a target domain and others, the ...
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Fastest Multimodal sampler

I am currently working with Multinest, a bayesian multimodal sampler however it becomes slow for higher dimensions, exponentially slow. Is there another sampler out there that can give me parameter ...
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1answer
116 views

Confusion about multimodal machine learning

I recently browsed through this tutorial on multimodal data. Attention: Multimodal in the sense of feature of very different type, that express the same thing -think picture and voice of ...
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0answers
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Is the Latent Dirichlet Allocation topic posterior multimodal?

In fitting the Latent Dirichlet Allocation with collapsed Gibbs sampling one builds a sampled approximation to the topic posterior distribution, $P(z|w)$ and use that to calculate the topic and word ...
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0answers
35 views

Multimodality of mixtures of more than two Normal distributions

Let $$\phi(x;\mu,\sigma) = \frac{1}{\sigma \sqrt{2\pi}} \exp \left(- \frac{(x-\mu)^2}{2\sigma^2}\right)$$ denote the Gaussian density function ($\sigma > 0$). Let $$f(x) = \sum_{i=1}^N p_i \...
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1answer
242 views

How to combine multiple signal data in my ML model?

I'm doing a task where I need to work with healthcare data from a few different sources. For example, one is an audio signal recording while another is biometric signal reading such as ECG. Both of ...
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35 views

How to numerically find the mode of a joint probability distribution from samples? [duplicate]

I have a large number of samples (say $N$) from a multimodal joint probability distribution, for example: ...
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191 views

Does it make sense to calculate MLE for multimodal distributions?

The simplest examples of multimodal distributions I've seen are mixtures, namely mixtures of normals. However, in this case, the Maximum Likelihood Estimator [MLE] doesn't make much sense. An example ...
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Distinguish between underlying Distribution and data shape in data transforming?

My question is not well worded, which is part of the problem. I’m specifically trying to apply this to my understanding of Six Sigma, but it probably applies everywhere. I know that having a normal ...
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30 views

inverse integration of multimodal distribution

I have a probability distribution, with a number of modes with different peak values, and I have to capture the 90% most significant value ranges. My idea is to apply a threshold starting from the ...
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0answers
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Is redundancy across different modalities required in multimodal machine learning

There are lots of articles available pertaining to 'multi-modal machine learning'. Among the major challenges, there is a one of representation i.e. "how to represent and summarize multi-modal data ...
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1answer
310 views

Evaluation of MCMC samples

My model contains five parameters. I want to make Bayesian estimation, but the Bayes estimates can not be obtained in closed form. So, I used Metropolis-Hastings to generate MCMC samples from ...
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1answer
380 views

mean variance of multimodal distribution

This may be too much of a simplistic question: but is it correct to say that it simply doesn't make sense to compute averages/means of data that is fundamentally multimodal? That is, there is not one ...
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1answer
87 views

Multimodality from unimodal variables

Let's say a data matrix $\bf{X} \in \mathbb{R}^{N \times D}$ has $D$ random variables each with $N$ observations. So $j$th column of $\bf{X}$ is $N$ observations of $j$th random variable. Suppose ...
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3answers
136 views

Detecting if an 1-dimenisional distribution is Multimodal

I'm writing up some C++ code for one of my Master's coursework. What I'm actually doing at the moment isn't on the syllabus, but I wish to implement it anyway as it will allow me to produce my own ...
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2answers
104 views

Is the likelihood of the sum of unimodal likelihoods also unimodal?

Let $p$ be a probability distribution and let $\mathcal{D}_1$, $\mathcal{D}_2$ be two sets of observations. If the likelihood of the parameter for some observations $$ \mathcal{L}(\theta; \mathcal{D})...
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1answer
124 views

How to detect multivariate binomial distributions?

I tried the hartigans dip test, and it works well for univariate distributions. However, when i tried taking each variable (dimension) and applied hartigans dip test (assuming that if along one ...
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2answers
500 views

Multimodal prior

In Bayesian method, a posterior can be either unimodal or multimodal. But, I cannot find any multimodal prior case yet. I wonder if it is possible, and there is any case that is using multimodal ...
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0answers
57 views

Describe why a distribution might be multimodal?

Suppose that I visualized a distirbution and noticed that it was multimodal. For example, I collected the heights of a bunch of students. I notice that the distirbution is multimodal. How would I ...